Digital twins for battery health prognosis: A comprehensive review of recent advances and challenges

Abstract

This review systematically examines the integration of Digital Twin (DT) technology with lithium-ion battery health prognosis systems. As electrification accelerates across multiple domains, accurate prediction of battery health indicators – including State of Charge (SOC), State of Health (SOH), Remaining Useful Life (RUL), and fault conditions – becomes increasingly critical for ensuring safety, reliability, and optimal performance. The core contribution of this review lies in proposing a novel four-layer conceptual framework, comprising the Physical, Data & Communication, Virtual Model, and Twin Service layers, as an analytical tool for structuring the field. After establishing the theoretical foundations of DTs and battery aging, we leverage this framework to systematically survey recent advancements in data augmentation, online state estimation, and fault diagnosis. Through this structured analysis, we then identify critical implementation challenges, including performance in extreme degradation phases, battery pack inconsistencies, and operation under complex conditions. We conclude by proposing future research directions focused on enhancing model generalization and creating standardized architectures through the integration of cloud computing and IoT technologies, and applying federated learning to solve potential privacy and security problems. This review serves as a critical reference by providing a structured, application-centric understanding of DTs in battery health management.